Building a RAG System That Runs Completely Offline

This guide shows how to build a fully offline Retrieval-Augmented Generation system that keeps sensitive documents on your machine. Using Ollama (Llama 3.2 for generation and nomic-embed-text for embeddings) plus FAISS for vector search, you’ll ingest PDFs/Markdown/HTML, chunk with overlap, embed locally, and answer questions with citations—no API keys, no usage fees, no data leaving your device after model downloads. The tutorial covers prerequisites, code for loaders/chunking/embeddings/vector DB/LLM, orchestration, and testing (FLoRA paper case study). Ideal for legal, medical, research, or enterprise teams that need strong privacy, predictable costs, and complete data control.